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opencv/modules/calib3d/src/fundam.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#include "_modelest.h"
using namespace cv;
template<typename T> int icvCompressPoints( T* ptr, const uchar* mask, int mstep, int count )
{
int i, j;
for( i = j = 0; i < count; i++ )
if( mask[i*mstep] )
{
if( i > j )
ptr[j] = ptr[i];
j++;
}
return j;
}
class CvHomographyEstimator : public CvModelEstimator2
{
public:
CvHomographyEstimator( int modelPoints );
virtual int runKernel( const CvMat* m1, const CvMat* m2, CvMat* model );
virtual bool refine( const CvMat* m1, const CvMat* m2,
CvMat* model, int maxIters );
protected:
virtual void computeReprojError( const CvMat* m1, const CvMat* m2,
const CvMat* model, CvMat* error );
};
CvHomographyEstimator::CvHomographyEstimator(int _modelPoints)
: CvModelEstimator2(_modelPoints, cvSize(3,3), 1)
{
assert( _modelPoints == 4 || _modelPoints == 5 );
checkPartialSubsets = false;
}
int CvHomographyEstimator::runKernel( const CvMat* m1, const CvMat* m2, CvMat* H )
{
int i, count = m1->rows*m1->cols;
const CvPoint2D64f* M = (const CvPoint2D64f*)m1->data.ptr;
const CvPoint2D64f* m = (const CvPoint2D64f*)m2->data.ptr;
double LtL[9][9], W[9][9], V[9][9];
CvMat _LtL = cvMat( 9, 9, CV_64F, LtL );
CvMat matW = cvMat( 9, 9, CV_64F, W );
CvMat matV = cvMat( 9, 9, CV_64F, V );
CvMat _H0 = cvMat( 3, 3, CV_64F, V[8] );
CvMat _Htemp = cvMat( 3, 3, CV_64F, V[7] );
CvPoint2D64f cM={0,0}, cm={0,0}, sM={0,0}, sm={0,0};
for( i = 0; i < count; i++ )
{
cm.x += m[i].x; cm.y += m[i].y;
cM.x += M[i].x; cM.y += M[i].y;
}
cm.x /= count; cm.y /= count;
cM.x /= count; cM.y /= count;
for( i = 0; i < count; i++ )
{
sm.x += fabs(m[i].x - cm.x);
sm.y += fabs(m[i].y - cm.y);
sM.x += fabs(M[i].x - cM.x);
sM.y += fabs(M[i].y - cM.y);
}
if( fabs(sm.x) < DBL_EPSILON || fabs(sm.y) < DBL_EPSILON ||
fabs(sM.x) < DBL_EPSILON || fabs(sM.y) < DBL_EPSILON )
return 0;
sm.x = count/sm.x; sm.y = count/sm.y;
sM.x = count/sM.x; sM.y = count/sM.y;
double invHnorm[9] = { 1./sm.x, 0, cm.x, 0, 1./sm.y, cm.y, 0, 0, 1 };
double Hnorm2[9] = { sM.x, 0, -cM.x*sM.x, 0, sM.y, -cM.y*sM.y, 0, 0, 1 };
CvMat _invHnorm = cvMat( 3, 3, CV_64FC1, invHnorm );
CvMat _Hnorm2 = cvMat( 3, 3, CV_64FC1, Hnorm2 );
cvZero( &_LtL );
for( i = 0; i < count; i++ )
{
double x = (m[i].x - cm.x)*sm.x, y = (m[i].y - cm.y)*sm.y;
double X = (M[i].x - cM.x)*sM.x, Y = (M[i].y - cM.y)*sM.y;
double Lx[] = { X, Y, 1, 0, 0, 0, -x*X, -x*Y, -x };
double Ly[] = { 0, 0, 0, X, Y, 1, -y*X, -y*Y, -y };
int j, k;
for( j = 0; j < 9; j++ )
for( k = j; k < 9; k++ )
LtL[j][k] += Lx[j]*Lx[k] + Ly[j]*Ly[k];
}
cvCompleteSymm( &_LtL );
//cvSVD( &_LtL, &matW, 0, &matV, CV_SVD_MODIFY_A + CV_SVD_V_T );
cvEigenVV( &_LtL, &matV, &matW );
cvMatMul( &_invHnorm, &_H0, &_Htemp );
cvMatMul( &_Htemp, &_Hnorm2, &_H0 );
cvConvertScale( &_H0, H, 1./_H0.data.db[8] );
return 1;
}
void CvHomographyEstimator::computeReprojError( const CvMat* m1, const CvMat* m2,
const CvMat* model, CvMat* _err )
{
int i, count = m1->rows*m1->cols;
const CvPoint2D64f* M = (const CvPoint2D64f*)m1->data.ptr;
const CvPoint2D64f* m = (const CvPoint2D64f*)m2->data.ptr;
const double* H = model->data.db;
float* err = _err->data.fl;
for( i = 0; i < count; i++ )
{
double ww = 1./(H[6]*M[i].x + H[7]*M[i].y + 1.);
double dx = (H[0]*M[i].x + H[1]*M[i].y + H[2])*ww - m[i].x;
double dy = (H[3]*M[i].x + H[4]*M[i].y + H[5])*ww - m[i].y;
err[i] = (float)(dx*dx + dy*dy);
}
}
bool CvHomographyEstimator::refine( const CvMat* m1, const CvMat* m2, CvMat* model, int maxIters )
{
CvLevMarq solver(8, 0, cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, maxIters, DBL_EPSILON));
int i, j, k, count = m1->rows*m1->cols;
const CvPoint2D64f* M = (const CvPoint2D64f*)m1->data.ptr;
const CvPoint2D64f* m = (const CvPoint2D64f*)m2->data.ptr;
CvMat modelPart = cvMat( solver.param->rows, solver.param->cols, model->type, model->data.ptr );
cvCopy( &modelPart, solver.param );
for(;;)
{
const CvMat* _param = 0;
CvMat *_JtJ = 0, *_JtErr = 0;
double* _errNorm = 0;
if( !solver.updateAlt( _param, _JtJ, _JtErr, _errNorm ))
break;
for( i = 0; i < count; i++ )
{
const double* h = _param->data.db;
double Mx = M[i].x, My = M[i].y;
double ww = h[6]*Mx + h[7]*My + 1.;
ww = fabs(ww) > DBL_EPSILON ? 1./ww : 0;
double _xi = (h[0]*Mx + h[1]*My + h[2])*ww;
double _yi = (h[3]*Mx + h[4]*My + h[5])*ww;
double err[] = { _xi - m[i].x, _yi - m[i].y };
if( _JtJ || _JtErr )
{
double J[][8] =
{
{ Mx*ww, My*ww, ww, 0, 0, 0, -Mx*ww*_xi, -My*ww*_xi },
{ 0, 0, 0, Mx*ww, My*ww, ww, -Mx*ww*_yi, -My*ww*_yi }
};
for( j = 0; j < 8; j++ )
{
for( k = j; k < 8; k++ )
_JtJ->data.db[j*8+k] += J[0][j]*J[0][k] + J[1][j]*J[1][k];
_JtErr->data.db[j] += J[0][j]*err[0] + J[1][j]*err[1];
}
}
if( _errNorm )
*_errNorm += err[0]*err[0] + err[1]*err[1];
}
}
cvCopy( solver.param, &modelPart );
return true;
}
CV_IMPL int
cvFindHomography( const CvMat* objectPoints, const CvMat* imagePoints,
CvMat* __H, int method, double ransacReprojThreshold,
CvMat* mask )
{
const double confidence = 0.995;
const int maxIters = 2000;
const double defaultRANSACReprojThreshold = 3;
bool result = false;
Ptr<CvMat> m, M, tempMask;
double H[9];
CvMat matH = cvMat( 3, 3, CV_64FC1, H );
int count;
CV_Assert( CV_IS_MAT(imagePoints) && CV_IS_MAT(objectPoints) );
count = MAX(imagePoints->cols, imagePoints->rows);
CV_Assert( count >= 4 );
if( ransacReprojThreshold <= 0 )
ransacReprojThreshold = defaultRANSACReprojThreshold;
m = cvCreateMat( 1, count, CV_64FC2 );
cvConvertPointsHomogeneous( imagePoints, m );
M = cvCreateMat( 1, count, CV_64FC2 );
cvConvertPointsHomogeneous( objectPoints, M );
if( mask )
{
CV_Assert( CV_IS_MASK_ARR(mask) && CV_IS_MAT_CONT(mask->type) &&
(mask->rows == 1 || mask->cols == 1) &&
mask->rows*mask->cols == count );
}
if( mask || count > 4 )
tempMask = cvCreateMat( 1, count, CV_8U );
if( !tempMask.empty() )
cvSet( tempMask, cvScalarAll(1.) );
CvHomographyEstimator estimator( MIN(count, 4) );
if( count == 4 )
method = 0;
if( method == CV_LMEDS )
result = estimator.runLMeDS( M, m, &matH, tempMask, confidence, maxIters );
else if( method == CV_RANSAC )
result = estimator.runRANSAC( M, m, &matH, tempMask, ransacReprojThreshold, confidence, maxIters);
else
result = estimator.runKernel( M, m, &matH ) > 0;
if( result && count > 4 )
{
icvCompressPoints( (CvPoint2D64f*)M->data.ptr, tempMask->data.ptr, 1, count );
count = icvCompressPoints( (CvPoint2D64f*)m->data.ptr, tempMask->data.ptr, 1, count );
M->cols = m->cols = count;
if( method == CV_RANSAC )
estimator.runKernel( M, m, &matH );
estimator.refine( M, m, &matH, 10 );
}
if( result )
cvConvert( &matH, __H );
if( mask && tempMask )
{
if( CV_ARE_SIZES_EQ(mask, tempMask) )
cvCopy( tempMask, mask );
else
cvTranspose( tempMask, mask );
}
return (int)result;
}
/* Evaluation of Fundamental Matrix from point correspondences.
The original code has been written by Valery Mosyagin */
/* The algorithms (except for RANSAC) and the notation have been taken from
Zhengyou Zhang's research report
"Determining the Epipolar Geometry and its Uncertainty: A Review"
that can be found at http://www-sop.inria.fr/robotvis/personnel/zzhang/zzhang-eng.html */
/************************************** 7-point algorithm *******************************/
class CvFMEstimator : public CvModelEstimator2
{
public:
CvFMEstimator( int _modelPoints );
virtual int runKernel( const CvMat* m1, const CvMat* m2, CvMat* model );
virtual int run7Point( const CvMat* m1, const CvMat* m2, CvMat* model );
virtual int run8Point( const CvMat* m1, const CvMat* m2, CvMat* model );
protected:
virtual void computeReprojError( const CvMat* m1, const CvMat* m2,
const CvMat* model, CvMat* error );
};
CvFMEstimator::CvFMEstimator( int _modelPoints )
: CvModelEstimator2( _modelPoints, cvSize(3,3), _modelPoints == 7 ? 3 : 1 )
{
assert( _modelPoints == 7 || _modelPoints == 8 );
}
int CvFMEstimator::runKernel( const CvMat* m1, const CvMat* m2, CvMat* model )
{
return modelPoints == 7 ? run7Point( m1, m2, model ) : run8Point( m1, m2, model );
}
int CvFMEstimator::run7Point( const CvMat* _m1, const CvMat* _m2, CvMat* _fmatrix )
{
double a[7*9], w[7], v[9*9], c[4], r[3];
double* f1, *f2;
double t0, t1, t2;
CvMat A = cvMat( 7, 9, CV_64F, a );
CvMat V = cvMat( 9, 9, CV_64F, v );
CvMat W = cvMat( 7, 1, CV_64F, w );
CvMat coeffs = cvMat( 1, 4, CV_64F, c );
CvMat roots = cvMat( 1, 3, CV_64F, r );
const CvPoint2D64f* m1 = (const CvPoint2D64f*)_m1->data.ptr;
const CvPoint2D64f* m2 = (const CvPoint2D64f*)_m2->data.ptr;
double* fmatrix = _fmatrix->data.db;
int i, k, n;
// form a linear system: i-th row of A(=a) represents
// the equation: (m2[i], 1)'*F*(m1[i], 1) = 0
for( i = 0; i < 7; i++ )
{
double x0 = m1[i].x, y0 = m1[i].y;
double x1 = m2[i].x, y1 = m2[i].y;
a[i*9+0] = x1*x0;
a[i*9+1] = x1*y0;
a[i*9+2] = x1;
a[i*9+3] = y1*x0;
a[i*9+4] = y1*y0;
a[i*9+5] = y1;
a[i*9+6] = x0;
a[i*9+7] = y0;
a[i*9+8] = 1;
}
// A*(f11 f12 ... f33)' = 0 is singular (7 equations for 9 variables), so
// the solution is linear subspace of dimensionality 2.
// => use the last two singular vectors as a basis of the space
// (according to SVD properties)
cvSVD( &A, &W, 0, &V, CV_SVD_MODIFY_A + CV_SVD_V_T );
f1 = v + 7*9;
f2 = v + 8*9;
// f1, f2 is a basis => lambda*f1 + mu*f2 is an arbitrary f. matrix.
// as it is determined up to a scale, normalize lambda & mu (lambda + mu = 1),
// so f ~ lambda*f1 + (1 - lambda)*f2.
// use the additional constraint det(f) = det(lambda*f1 + (1-lambda)*f2) to find lambda.
// it will be a cubic equation.
// find c - polynomial coefficients.
for( i = 0; i < 9; i++ )
f1[i] -= f2[i];
t0 = f2[4]*f2[8] - f2[5]*f2[7];
t1 = f2[3]*f2[8] - f2[5]*f2[6];
t2 = f2[3]*f2[7] - f2[4]*f2[6];
c[3] = f2[0]*t0 - f2[1]*t1 + f2[2]*t2;
c[2] = f1[0]*t0 - f1[1]*t1 + f1[2]*t2 -
f1[3]*(f2[1]*f2[8] - f2[2]*f2[7]) +
f1[4]*(f2[0]*f2[8] - f2[2]*f2[6]) -
f1[5]*(f2[0]*f2[7] - f2[1]*f2[6]) +
f1[6]*(f2[1]*f2[5] - f2[2]*f2[4]) -
f1[7]*(f2[0]*f2[5] - f2[2]*f2[3]) +
f1[8]*(f2[0]*f2[4] - f2[1]*f2[3]);
t0 = f1[4]*f1[8] - f1[5]*f1[7];
t1 = f1[3]*f1[8] - f1[5]*f1[6];
t2 = f1[3]*f1[7] - f1[4]*f1[6];
c[1] = f2[0]*t0 - f2[1]*t1 + f2[2]*t2 -
f2[3]*(f1[1]*f1[8] - f1[2]*f1[7]) +
f2[4]*(f1[0]*f1[8] - f1[2]*f1[6]) -
f2[5]*(f1[0]*f1[7] - f1[1]*f1[6]) +
f2[6]*(f1[1]*f1[5] - f1[2]*f1[4]) -
f2[7]*(f1[0]*f1[5] - f1[2]*f1[3]) +
f2[8]*(f1[0]*f1[4] - f1[1]*f1[3]);
c[0] = f1[0]*t0 - f1[1]*t1 + f1[2]*t2;
// solve the cubic equation; there can be 1 to 3 roots ...
n = cvSolveCubic( &coeffs, &roots );
if( n < 1 || n > 3 )
return n;
for( k = 0; k < n; k++, fmatrix += 9 )
{
// for each root form the fundamental matrix
double lambda = r[k], mu = 1.;
double s = f1[8]*r[k] + f2[8];
// normalize each matrix, so that F(3,3) (~fmatrix[8]) == 1
if( fabs(s) > DBL_EPSILON )
{
mu = 1./s;
lambda *= mu;
fmatrix[8] = 1.;
}
else
fmatrix[8] = 0.;
for( i = 0; i < 8; i++ )
fmatrix[i] = f1[i]*lambda + f2[i]*mu;
}
return n;
}
int CvFMEstimator::run8Point( const CvMat* _m1, const CvMat* _m2, CvMat* _fmatrix )
{
double a[9*9], w[9], v[9*9];
CvMat W = cvMat( 1, 9, CV_64F, w );
CvMat V = cvMat( 9, 9, CV_64F, v );
CvMat A = cvMat( 9, 9, CV_64F, a );
CvMat U, F0, TF;
CvPoint2D64f m0c = {0,0}, m1c = {0,0};
double t, scale0 = 0, scale1 = 0;
const CvPoint2D64f* m1 = (const CvPoint2D64f*)_m1->data.ptr;
const CvPoint2D64f* m2 = (const CvPoint2D64f*)_m2->data.ptr;
double* fmatrix = _fmatrix->data.db;
CV_Assert( (_m1->cols == 1 || _m1->rows == 1) && CV_ARE_SIZES_EQ(_m1, _m2));
int i, j, k, count = _m1->cols*_m1->rows;
// compute centers and average distances for each of the two point sets
for( i = 0; i < count; i++ )
{
double x = m1[i].x, y = m1[i].y;
m0c.x += x; m0c.y += y;
x = m2[i].x, y = m2[i].y;
m1c.x += x; m1c.y += y;
}
// calculate the normalizing transformations for each of the point sets:
// after the transformation each set will have the mass center at the coordinate origin
// and the average distance from the origin will be ~sqrt(2).
t = 1./count;
m0c.x *= t; m0c.y *= t;
m1c.x *= t; m1c.y *= t;
for( i = 0; i < count; i++ )
{
double x = m1[i].x - m0c.x, y = m1[i].y - m0c.y;
scale0 += sqrt(x*x + y*y);
x = m2[i].x - m1c.x, y = m2[i].y - m1c.y;
scale1 += sqrt(x*x + y*y);
}
scale0 *= t;
scale1 *= t;
if( scale0 < FLT_EPSILON || scale1 < FLT_EPSILON )
return 0;
scale0 = sqrt(2.)/scale0;
scale1 = sqrt(2.)/scale1;
cvZero( &A );
// form a linear system Ax=0: for each selected pair of points m1 & m2,
// the row of A(=a) represents the coefficients of equation: (m2, 1)'*F*(m1, 1) = 0
// to save computation time, we compute (At*A) instead of A and then solve (At*A)x=0.
for( i = 0; i < count; i++ )
{
double x0 = (m1[i].x - m0c.x)*scale0;
double y0 = (m1[i].y - m0c.y)*scale0;
double x1 = (m2[i].x - m1c.x)*scale1;
double y1 = (m2[i].y - m1c.y)*scale1;
double r[9] = { x1*x0, x1*y0, x1, y1*x0, y1*y0, y1, x0, y0, 1 };
for( j = 0; j < 9; j++ )
for( k = 0; k < 9; k++ )
a[j*9+k] += r[j]*r[k];
}
cvSVD( &A, &W, 0, &V, CV_SVD_MODIFY_A + CV_SVD_V_T );
for( i = 0; i < 8; i++ )
{
if( fabs(w[i]) < DBL_EPSILON )
break;
}
if( i < 7 )
return 0;
F0 = cvMat( 3, 3, CV_64F, v + 9*8 ); // take the last column of v as a solution of Af = 0
// make F0 singular (of rank 2) by decomposing it with SVD,
// zeroing the last diagonal element of W and then composing the matrices back.
// use v as a temporary storage for different 3x3 matrices
W = U = V = TF = F0;
W.data.db = v;
U.data.db = v + 9;
V.data.db = v + 18;
TF.data.db = v + 27;
cvSVD( &F0, &W, &U, &V, CV_SVD_MODIFY_A + CV_SVD_U_T + CV_SVD_V_T );
W.data.db[8] = 0.;
// F0 <- U*diag([W(1), W(2), 0])*V'
cvGEMM( &U, &W, 1., 0, 0., &TF, CV_GEMM_A_T );
cvGEMM( &TF, &V, 1., 0, 0., &F0, 0/*CV_GEMM_B_T*/ );
// apply the transformation that is inverse
// to what we used to normalize the point coordinates
{
double tt0[] = { scale0, 0, -scale0*m0c.x, 0, scale0, -scale0*m0c.y, 0, 0, 1 };
double tt1[] = { scale1, 0, -scale1*m1c.x, 0, scale1, -scale1*m1c.y, 0, 0, 1 };
CvMat T0, T1;
T0 = T1 = F0;
T0.data.db = tt0;
T1.data.db = tt1;
// F0 <- T1'*F0*T0
cvGEMM( &T1, &F0, 1., 0, 0., &TF, CV_GEMM_A_T );
F0.data.db = fmatrix;
cvGEMM( &TF, &T0, 1., 0, 0., &F0, 0 );
// make F(3,3) = 1
if( fabs(F0.data.db[8]) > FLT_EPSILON )
cvScale( &F0, &F0, 1./F0.data.db[8] );
}
return 1;
}
void CvFMEstimator::computeReprojError( const CvMat* _m1, const CvMat* _m2,
const CvMat* model, CvMat* _err )
{
int i, count = _m1->rows*_m1->cols;
const CvPoint2D64f* m1 = (const CvPoint2D64f*)_m1->data.ptr;
const CvPoint2D64f* m2 = (const CvPoint2D64f*)_m2->data.ptr;
const double* F = model->data.db;
float* err = _err->data.fl;
for( i = 0; i < count; i++ )
{
double a, b, c, d1, d2, s1, s2;
a = F[0]*m1[i].x + F[1]*m1[i].y + F[2];
b = F[3]*m1[i].x + F[4]*m1[i].y + F[5];
c = F[6]*m1[i].x + F[7]*m1[i].y + F[8];
s2 = 1./(a*a + b*b);
d2 = m2[i].x*a + m2[i].y*b + c;
a = F[0]*m2[i].x + F[3]*m2[i].y + F[6];
b = F[1]*m2[i].x + F[4]*m2[i].y + F[7];
c = F[2]*m2[i].x + F[5]*m2[i].y + F[8];
s1 = 1./(a*a + b*b);
d1 = m1[i].x*a + m1[i].y*b + c;
err[i] = (float)std::max(d1*d1*s1, d2*d2*s2);
}
}
CV_IMPL int cvFindFundamentalMat( const CvMat* points1, const CvMat* points2,
CvMat* fmatrix, int method,
double param1, double param2, CvMat* mask )
{
int result = 0;
Ptr<CvMat> m1, m2, tempMask;
double F[3*9];
CvMat _F3x3 = cvMat( 3, 3, CV_64FC1, F ), _F9x3 = cvMat( 9, 3, CV_64FC1, F );
int count;
CV_Assert( CV_IS_MAT(points1) && CV_IS_MAT(points2) && CV_ARE_SIZES_EQ(points1, points2) );
CV_Assert( CV_IS_MAT(fmatrix) && fmatrix->cols == 3 &&
(fmatrix->rows == 3 || (fmatrix->rows == 9 && method == CV_FM_7POINT)) );
count = MAX(points1->cols, points1->rows);
if( count < 7 )
return 0;
m1 = cvCreateMat( 1, count, CV_64FC2 );
cvConvertPointsHomogeneous( points1, m1 );
m2 = cvCreateMat( 1, count, CV_64FC2 );
cvConvertPointsHomogeneous( points2, m2 );
if( mask )
{
CV_Assert( CV_IS_MASK_ARR(mask) && CV_IS_MAT_CONT(mask->type) &&
(mask->rows == 1 || mask->cols == 1) &&
mask->rows*mask->cols == count );
}
if( mask || count > 8 )
tempMask = cvCreateMat( 1, count, CV_8U );
if( !tempMask.empty() )
cvSet( tempMask, cvScalarAll(1.) );
CvFMEstimator estimator(7);
if( count == 7 )
result = estimator.run7Point(m1, m2, &_F9x3);
else if( count == 8 || method == CV_FM_8POINT )
result = estimator.run8Point(m1, m2, &_F3x3);
else if( count >= 8 )
{
if( param1 <= 0 )
param1 = 3;
if( param2 < DBL_EPSILON || param2 > 1 - DBL_EPSILON )
param2 = 0.99;
if( (method & ~3) == CV_RANSAC && count >= 15 )
result = estimator.runRANSAC(m1, m2, &_F3x3, tempMask, param1, param2 );
else
result = estimator.runLMeDS(m1, m2, &_F3x3, tempMask, param2 );
if( result <= 0 )
return 0;
/*icvCompressPoints( (CvPoint2D64f*)m1->data.ptr, tempMask->data.ptr, 1, count );
count = icvCompressPoints( (CvPoint2D64f*)m2->data.ptr, tempMask->data.ptr, 1, count );
assert( count >= 8 );
m1->cols = m2->cols = count;
estimator.run8Point(m1, m2, &_F3x3);*/
}
if( result )
cvConvert( fmatrix->rows == 3 ? &_F3x3 : &_F9x3, fmatrix );
if( mask && tempMask )
{
if( CV_ARE_SIZES_EQ(mask, tempMask) )
cvCopy( tempMask, mask );
else
cvTranspose( tempMask, mask );
}
return result;
}
CV_IMPL void cvComputeCorrespondEpilines( const CvMat* points, int pointImageID,
const CvMat* fmatrix, CvMat* lines )
{
int abc_stride, abc_plane_stride, abc_elem_size;
int plane_stride, stride, elem_size;
int i, dims, count, depth, cn, abc_dims, abc_count, abc_depth, abc_cn;
uchar *ap, *bp, *cp;
const uchar *xp, *yp, *zp;
double f[9];
CvMat F = cvMat( 3, 3, CV_64F, f );
if( !CV_IS_MAT(points) )
CV_Error( !points ? CV_StsNullPtr : CV_StsBadArg, "points parameter is not a valid matrix" );
depth = CV_MAT_DEPTH(points->type);
cn = CV_MAT_CN(points->type);
if( (depth != CV_32F && depth != CV_64F) || (cn != 1 && cn != 2 && cn != 3) )
CV_Error( CV_StsUnsupportedFormat, "The format of point matrix is unsupported" );
if( points->rows > points->cols )
{
dims = cn*points->cols;
count = points->rows;
}
else
{
if( (points->rows > 1 && cn > 1) || (points->rows == 1 && cn == 1) )
CV_Error( CV_StsBadSize, "The point matrix does not have a proper layout (2xn, 3xn, nx2 or nx3)" );
dims = cn * points->rows;
count = points->cols;
}
if( dims != 2 && dims != 3 )
CV_Error( CV_StsOutOfRange, "The dimensionality of points must be 2 or 3" );
if( !CV_IS_MAT(fmatrix) )
CV_Error( !fmatrix ? CV_StsNullPtr : CV_StsBadArg, "fmatrix is not a valid matrix" );
if( CV_MAT_TYPE(fmatrix->type) != CV_32FC1 && CV_MAT_TYPE(fmatrix->type) != CV_64FC1 )
CV_Error( CV_StsUnsupportedFormat, "fundamental matrix must have 32fC1 or 64fC1 type" );
if( fmatrix->cols != 3 || fmatrix->rows != 3 )
CV_Error( CV_StsBadSize, "fundamental matrix must be 3x3" );
if( !CV_IS_MAT(lines) )
CV_Error( !lines ? CV_StsNullPtr : CV_StsBadArg, "lines parameter is not a valid matrix" );
abc_depth = CV_MAT_DEPTH(lines->type);
abc_cn = CV_MAT_CN(lines->type);
if( (abc_depth != CV_32F && abc_depth != CV_64F) || (abc_cn != 1 && abc_cn != 3) )
CV_Error( CV_StsUnsupportedFormat, "The format of the matrix of lines is unsupported" );
if( lines->rows > lines->cols )
{
abc_dims = abc_cn*lines->cols;
abc_count = lines->rows;
}
else
{
if( (lines->rows > 1 && abc_cn > 1) || (lines->rows == 1 && abc_cn == 1) )
CV_Error( CV_StsBadSize, "The lines matrix does not have a proper layout (3xn or nx3)" );
abc_dims = abc_cn * lines->rows;
abc_count = lines->cols;
}
if( abc_dims != 3 )
CV_Error( CV_StsOutOfRange, "The lines matrix does not have a proper layout (3xn or nx3)" );
if( abc_count != count )
CV_Error( CV_StsUnmatchedSizes, "The numbers of points and lines are different" );
elem_size = CV_ELEM_SIZE(depth);
abc_elem_size = CV_ELEM_SIZE(abc_depth);
if( points->rows == dims )
{
plane_stride = points->step;
stride = elem_size;
}
else
{
plane_stride = elem_size;
stride = points->rows == 1 ? dims*elem_size : points->step;
}
if( lines->rows == 3 )
{
abc_plane_stride = lines->step;
abc_stride = abc_elem_size;
}
else
{
abc_plane_stride = abc_elem_size;
abc_stride = lines->rows == 1 ? 3*abc_elem_size : lines->step;
}
cvConvert( fmatrix, &F );
if( pointImageID == 2 )
cvTranspose( &F, &F );
xp = points->data.ptr;
yp = xp + plane_stride;
zp = dims == 3 ? yp + plane_stride : 0;
ap = lines->data.ptr;
bp = ap + abc_plane_stride;
cp = bp + abc_plane_stride;
for( i = 0; i < count; i++ )
{
double x, y, z = 1.;
double a, b, c, nu;
if( depth == CV_32F )
{
x = *(float*)xp; y = *(float*)yp;
if( zp )
z = *(float*)zp, zp += stride;
}
else
{
x = *(double*)xp; y = *(double*)yp;
if( zp )
z = *(double*)zp, zp += stride;
}
xp += stride; yp += stride;
a = f[0]*x + f[1]*y + f[2]*z;
b = f[3]*x + f[4]*y + f[5]*z;
c = f[6]*x + f[7]*y + f[8]*z;
nu = a*a + b*b;
nu = nu ? 1./sqrt(nu) : 1.;
a *= nu; b *= nu; c *= nu;
if( abc_depth == CV_32F )
{
*(float*)ap = (float)a;
*(float*)bp = (float)b;
*(float*)cp = (float)c;
}
else
{
*(double*)ap = a;
*(double*)bp = b;
*(double*)cp = c;
}
ap += abc_stride;
bp += abc_stride;
cp += abc_stride;
}
}
CV_IMPL void cvConvertPointsHomogeneous( const CvMat* src, CvMat* dst )
{
Ptr<CvMat> temp, denom;
int i, s_count, s_dims, d_count, d_dims;
CvMat _src, _dst, _ones;
CvMat* ones = 0;
if( !CV_IS_MAT(src) )
CV_Error( !src ? CV_StsNullPtr : CV_StsBadArg,
"The input parameter is not a valid matrix" );
if( !CV_IS_MAT(dst) )
CV_Error( !dst ? CV_StsNullPtr : CV_StsBadArg,
"The output parameter is not a valid matrix" );
if( src == dst || src->data.ptr == dst->data.ptr )
{
if( src != dst && (!CV_ARE_TYPES_EQ(src, dst) || !CV_ARE_SIZES_EQ(src,dst)) )
CV_Error( CV_StsBadArg, "Invalid inplace operation" );
return;
}
if( src->rows > src->cols )
{
if( !((src->cols > 1) ^ (CV_MAT_CN(src->type) > 1)) )
CV_Error( CV_StsBadSize, "Either the number of channels or columns or rows must be =1" );
s_dims = CV_MAT_CN(src->type)*src->cols;
s_count = src->rows;
}
else
{
if( !((src->rows > 1) ^ (CV_MAT_CN(src->type) > 1)) )
CV_Error( CV_StsBadSize, "Either the number of channels or columns or rows must be =1" );
s_dims = CV_MAT_CN(src->type)*src->rows;
s_count = src->cols;
}
if( src->rows == 1 || src->cols == 1 )
src = cvReshape( src, &_src, 1, s_count );
if( dst->rows > dst->cols )
{
if( !((dst->cols > 1) ^ (CV_MAT_CN(dst->type) > 1)) )
CV_Error( CV_StsBadSize,
"Either the number of channels or columns or rows in the input matrix must be =1" );
d_dims = CV_MAT_CN(dst->type)*dst->cols;
d_count = dst->rows;
}
else
{
if( !((dst->rows > 1) ^ (CV_MAT_CN(dst->type) > 1)) )
CV_Error( CV_StsBadSize,
"Either the number of channels or columns or rows in the output matrix must be =1" );
d_dims = CV_MAT_CN(dst->type)*dst->rows;
d_count = dst->cols;
}
if( dst->rows == 1 || dst->cols == 1 )
dst = cvReshape( dst, &_dst, 1, d_count );
if( s_count != d_count )
CV_Error( CV_StsUnmatchedSizes, "Both matrices must have the same number of points" );
if( CV_MAT_DEPTH(src->type) < CV_32F || CV_MAT_DEPTH(dst->type) < CV_32F )
CV_Error( CV_StsUnsupportedFormat,
"Both matrices must be floating-point (single or double precision)" );
if( s_dims < 2 || s_dims > 4 || d_dims < 2 || d_dims > 4 )
CV_Error( CV_StsOutOfRange,
"Both input and output point dimensionality must be 2, 3 or 4" );
if( s_dims < d_dims - 1 || s_dims > d_dims + 1 )
CV_Error( CV_StsUnmatchedSizes,
"The dimensionalities of input and output point sets differ too much" );
if( s_dims == d_dims - 1 )
{
if( d_count == dst->rows )
{
ones = cvGetSubRect( dst, &_ones, cvRect( s_dims, 0, 1, d_count ));
dst = cvGetSubRect( dst, &_dst, cvRect( 0, 0, s_dims, d_count ));
}
else
{
ones = cvGetSubRect( dst, &_ones, cvRect( 0, s_dims, d_count, 1 ));
dst = cvGetSubRect( dst, &_dst, cvRect( 0, 0, d_count, s_dims ));
}
}
if( s_dims <= d_dims )
{
if( src->rows == dst->rows && src->cols == dst->cols )
{
if( CV_ARE_TYPES_EQ( src, dst ) )
cvCopy( src, dst );
else
cvConvert( src, dst );
}
else
{
if( !CV_ARE_TYPES_EQ( src, dst ))
{
temp = cvCreateMat( src->rows, src->cols, dst->type );
cvConvert( src, temp );
src = temp;
}
cvTranspose( src, dst );
}
if( ones )
cvSet( ones, cvRealScalar(1.) );
}
else
{
int s_plane_stride, s_stride, d_plane_stride, d_stride, elem_size;
if( !CV_ARE_TYPES_EQ( src, dst ))
{
temp = cvCreateMat( src->rows, src->cols, dst->type );
cvConvert( src, temp );
src = temp;
}
elem_size = CV_ELEM_SIZE(src->type);
if( s_count == src->cols )
s_plane_stride = src->step / elem_size, s_stride = 1;
else
s_stride = src->step / elem_size, s_plane_stride = 1;
if( d_count == dst->cols )
d_plane_stride = dst->step / elem_size, d_stride = 1;
else
d_stride = dst->step / elem_size, d_plane_stride = 1;
denom = cvCreateMat( 1, d_count, dst->type );
if( CV_MAT_DEPTH(dst->type) == CV_32F )
{
const float* xs = src->data.fl;
const float* ys = xs + s_plane_stride;
const float* zs = 0;
const float* ws = xs + (s_dims - 1)*s_plane_stride;
float* iw = denom->data.fl;
float* xd = dst->data.fl;
float* yd = xd + d_plane_stride;
float* zd = 0;
if( d_dims == 3 )
{
zs = ys + s_plane_stride;
zd = yd + d_plane_stride;
}
for( i = 0; i < d_count; i++, ws += s_stride )
{
float t = *ws;
iw[i] = fabs((double)t) > FLT_EPSILON ? t : 1.f;
}
cvDiv( 0, denom, denom );
if( d_dims == 3 )
for( i = 0; i < d_count; i++ )
{
float w = iw[i];
float x = *xs * w, y = *ys * w, z = *zs * w;
xs += s_stride; ys += s_stride; zs += s_stride;
*xd = x; *yd = y; *zd = z;
xd += d_stride; yd += d_stride; zd += d_stride;
}
else
for( i = 0; i < d_count; i++ )
{
float w = iw[i];
float x = *xs * w, y = *ys * w;
xs += s_stride; ys += s_stride;
*xd = x; *yd = y;
xd += d_stride; yd += d_stride;
}
}
else
{
const double* xs = src->data.db;
const double* ys = xs + s_plane_stride;
const double* zs = 0;
const double* ws = xs + (s_dims - 1)*s_plane_stride;
double* iw = denom->data.db;
double* xd = dst->data.db;
double* yd = xd + d_plane_stride;
double* zd = 0;
if( d_dims == 3 )
{
zs = ys + s_plane_stride;
zd = yd + d_plane_stride;
}
for( i = 0; i < d_count; i++, ws += s_stride )
{
double t = *ws;
iw[i] = fabs(t) > DBL_EPSILON ? t : 1.;
}
cvDiv( 0, denom, denom );
if( d_dims == 3 )
for( i = 0; i < d_count; i++ )
{
double w = iw[i];
double x = *xs * w, y = *ys * w, z = *zs * w;
xs += s_stride; ys += s_stride; zs += s_stride;
*xd = x; *yd = y; *zd = z;
xd += d_stride; yd += d_stride; zd += d_stride;
}
else
for( i = 0; i < d_count; i++ )
{
double w = iw[i];
double x = *xs * w, y = *ys * w;
xs += s_stride; ys += s_stride;
*xd = x; *yd = y;
xd += d_stride; yd += d_stride;
}
}
}
}
cv::Mat cv::findHomography( InputArray _points1, InputArray _points2,
int method, double ransacReprojThreshold, OutputArray _mask )
{
Mat points1 = _points1.getMat(), points2 = _points2.getMat();
int npoints = points1.checkVector(2);
CV_Assert( npoints >= 0 && points2.checkVector(2) == npoints &&
points1.type() == points2.type());
Mat H(3, 3, CV_64F);
CvMat _pt1 = points1, _pt2 = points2;
CvMat matH = H, c_mask, *p_mask = 0;
if( _mask.needed() )
{
_mask.create(npoints, 1, CV_8U, -1, true);
p_mask = &(c_mask = _mask.getMat());
}
bool ok = cvFindHomography( &_pt1, &_pt2, &matH, method, ransacReprojThreshold, p_mask ) > 0;
if( !ok )
H = Scalar(0);
return H;
}
cv::Mat cv::findHomography( InputArray _points1, InputArray _points2,
OutputArray _mask, int method, double ransacReprojThreshold )
{
return cv::findHomography(_points1, _points2, method, ransacReprojThreshold, _mask);
}
cv::Mat cv::findFundamentalMat( InputArray _points1, InputArray _points2,
int method, double param1, double param2,
OutputArray _mask )
{
Mat points1 = _points1.getMat(), points2 = _points2.getMat();
int npoints = points1.checkVector(2);
CV_Assert( npoints >= 0 && points2.checkVector(2) == npoints &&
points1.type() == points2.type());
Mat F(3, 3, CV_64F);
CvMat _pt1 = points1, _pt2 = points2;
CvMat matF = F, c_mask, *p_mask = 0;
if( _mask.needed() )
{
_mask.create(npoints, 1, CV_8U, -1, true);
p_mask = &(c_mask = _mask.getMat());
}
int n = cvFindFundamentalMat( &_pt1, &_pt2, &matF, method, param1, param2, p_mask );
if( n <= 0 )
F = Scalar(0);
return F;
}
cv::Mat cv::findFundamentalMat( InputArray _points1, InputArray _points2,
OutputArray _mask, int method, double param1, double param2 )
{
return cv::findFundamentalMat(_points1, _points2, method, param1, param2, _mask);
}
void cv::computeCorrespondEpilines( InputArray _points, int whichImage,
InputArray _Fmat, OutputArray _lines )
{
Mat points = _points.getMat(), F = _Fmat.getMat();
int npoints = points.checkVector(2);
CV_Assert( npoints >= 0 && (points.depth() == CV_32F || points.depth() == CV_32S));
_lines.create(npoints, 1, CV_32FC3, -1, true);
CvMat c_points = points, c_lines = _lines.getMat(), c_F = F;
cvComputeCorrespondEpilines(&c_points, whichImage, &c_F, &c_lines);
}
void cv::convertPointsFromHomogeneous( InputArray _src, OutputArray _dst )
{
Mat src = _src.getMat();
int npoints = src.checkVector(3), cn = 3;
if( npoints < 0 )
{
npoints = src.checkVector(4);
if( npoints >= 0 )
cn = 4;
}
CV_Assert( npoints >= 0 && (src.depth() == CV_32F || src.depth() == CV_32S));
_dst.create(npoints, 1, CV_MAKETYPE(CV_32F, cn-1));
CvMat c_src = src, c_dst = _dst.getMat();
cvConvertPointsHomogeneous(&c_src, &c_dst);
}
void cv::convertPointsToHomogeneous( InputArray _src, OutputArray _dst )
{
Mat src = _src.getMat();
int npoints = src.checkVector(2), cn = 2;
if( npoints < 0 )
{
npoints = src.checkVector(3);
if( npoints >= 0 )
cn = 3;
}
CV_Assert( npoints >= 0 && (src.depth() == CV_32F || src.depth() == CV_32S));
_dst.create(npoints, 1, CV_MAKETYPE(CV_32F, cn+1));
CvMat c_src = src, c_dst = _dst.getMat();
cvConvertPointsHomogeneous(&c_src, &c_dst);
}
void cv::convertPointsHomogeneous( InputArray _src, OutputArray _dst )
{
int stype = _src.type(), dtype = _dst.type();
CV_Assert( _dst.fixedType() );
if( CV_MAT_CN(stype) > CV_MAT_CN(dtype) )
convertPointsFromHomogeneous(_src, _dst);
else
convertPointsToHomogeneous(_src, _dst);
}
/* End of file. */
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